Principal AI Engineer — ML MLOps Platform Architect

Job Description Principal Machine Learning Engineer (LLM Agentic AI & Model Platform)

Position Summary

The Principal Machine Learning Engineer is a senior technical leader responsible for architecting building and operationalizing enterprise-scale AI Generative AI and Agentic AI capabilities across Databricks Azure AI Foundry and cloud-native AI platforms. This role will lead the strategy architecture and implementation of foundation models custom models AI platform services ModelOps and Agentic AI frameworks that power enterprise AI solutions.

The ideal candidate combines deep machine learning expertise with hands-on software engineering cloud architecture MLOps and platform engineering skills. They will drive model lifecycle management AI gateway architecture model routing strategies token optimization and enterprise AI governance while enabling secure scalable and cost-efficient AI adoption across the organization.

Key Responsibilities

Enterprise LLM Platform Leadership

• Own the enterprise strategy for foundation models frontier models and custom enterprise models.
• Evaluate benchmark onboard and operationalize leading AI models from OpenAI Anthropic Google Azure AI Foundry Databricks Mosaic AI and open-source ecosystems.
• Define model selection and deployment strategies based on performance cost security latency and business requirements.
• Establish enterprise standards for model consumption and governance.

Model Lifecycle Management (ModelOps)

• Architect and implement end-to-end model lifecycle management capabilities.
• Lead model training fine-tuning evaluation testing deployment monitoring optimization and retirement processes.
• Build automated ModelOps and MLOps pipelines to support enterprise-scale AI workloads.
• Implement model versioning lineage experimentation tracking model monitoring and drift detection frameworks.
• Ensure reproducibility compliance governance and auditability of AI models.

Agentic AI Architecture

• Design and implement enterprise Agentic AI architectures and frameworks.
• Develop multi-agent orchestration patterns planning frameworks tool integration reasoning workflows memory management and contextual intelligence capabilities.
• Define AgentOps standards for deployment monitoring evaluation and governance of autonomous agents.
• Establish reusable enterprise frameworks supporting scalable agent development and deployment.

AI Gateway & Model Routing

• Architect enterprise AI Gateway capabilities for secure model access and governance.
• Design LLM routing frameworks that dynamically select optimal models based on workload cost latency and performance requirements.
• Define model consumption patterns for applications APIs copilots and intelligent agents.
• Enable centralized access governance monitoring and observability across all AI services.
• Develop abstraction layers supporting seamless integration of multiple foundation models.

Token Optimization & AI FinOps

• Define token optimization strategies to improve AI cost efficiency and performance.
• Implement prompt engineering caching model tiering response optimization and intelligent routing techniques.
• Establish monitoring and reporting frameworks for model utilization token consumption and AI infrastructure costs.
• Drive AI FinOps initiatives and platform optimization strategies.

AI Platform Engineering

• Build and scale AI platform capabilities on Databricks Azure AI Foundry and Azure cloud platforms.
• Architect enterprise-ready model serving inference vector search RAG and semantic retrieval solutions.
• Develop reusable AI platform services accelerators SDKs and reference architectures.

• Enable secure and governed AI consumption across multiple business domains.

Cloud Infrastructure & Security

• Design cloud-native infrastructure for model training fine-tuning and large-scale inference workloads.
• Build GPU-enabled highly scalable resilient and secure AI environments.
• Implement Infrastructure as Code automated deployments and platform observability.
• Establish security-by-design principles for AI workloads including model security access controls secrets management and responsible AI controls.
• Collaborate with Security and Governance teams to ensure compliance with enterprise standards.

Technical Leadership

• Serve as the principal technical authority for Machine Learning LLMs Agentic AI and Model Platforms.
• Mentor AI Engineers ML Engineers Platform Engineers and Architects.
• Lead architecture reviews platform strategy discussions and technology evaluations.
• Drive innovation through proof-of-concepts and adoption of emerging AI technologies.
• Partner with business and technology leaders to accelerate AI transformation initiatives.

Required Technical Skills

Qualifications

• Bachelors or Masters degree in Computer Science Artificial Intelligence Data Science Engineering or related field.
• 12 years of experience in Software Engineering Machine Learning AI Platform Engineering or Cloud Architecture.
• 5 years of experience leading enterprise AI/ML platform implementations.
• Proven experience deploying and operating large-scale AI LLM and Agentic AI solutions in production environments.

Success Metrics

• Successful operationalization of frontier and custom foundation models.
• AI platform scalability reliability security and governance compliance.
• Reduced model deployment timelines through automated ModelOps capabilities.
• Optimized AI infrastructure and token consumption costs.
• Adoption of reusable AI platform patterns across business domains.
• Increased speed quality and business impact of AI and Agentic AI solutions
• Establishment of enterprise-grade AI architecture standards and engineering excellence.